Apparatus and method to monitor robot mechanical condition

ABSTRACT

Mechanical condition monitoring of robots can be used to detect unexpected failure of robots. Data taken from a robot operation is processed and compared against a health baseline. Features extracted during the monitoring stage of robot operation are aligned with features extracted during the training stage in which the health baseline is established by projecting both onto a common subspace. A classifier which can include a distance assessment such as an L2-norm is used within the common subspace to assess the condition of the robot. Excursions of the distance assessment from a criteria indicate a failure or potential failure.

TECHNICAL FIELD

The present invention generally relates to condition monitoring, andmore particularly, but not exclusively, to operation independent roboticcondition monitoring.

BACKGROUND

Current mechanical condition monitoring algorithms areoperation-dependent, which requires that a current robotic operationhaving a set of operational movements (such as those movement(s)associated with a production environment) be the same for comparisonpurposes as movements of the robotic operation used for establishing ahealthy baseline mathematical robot model. Such a requirement forsimilar movements can impede efficient operation since operationalmovements of the robot in an ongoing production environment must beceased (at least when the current robotic operation is not the same asthe operation used to establish the baseline) to allow a comparison withthe healthy baseline model. If an operator chooses to forgo maintenancemonitoring in order to increase uptime, an issue might develop, in theworst case resulting in the breakdown of the robot. Accordingly, thereremains a need for further contributions in this area of technology.

SUMMARY

One embodiment of the present invention is a unique condition monitoringdevice. Other embodiments include apparatuses, systems, devices,hardware, methods, and combinations for monitoring condition of a robotindependent of the particular robot operation. Further embodiments,forms, features, aspects, benefits, and advantages of the presentapplication shall become apparent from the description and figuresprovided herewith.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates one embodiment of a robot.

FIG. 2 illustrates an embodiment of a computing device.

FIG. 3 illustrates a health monitoring assessment approach.

FIG. 4 illustrates a health monitoring assessment approach.

FIG. 5 illustrates a comparison of data using techniques describedherein.

FIG. 6 illustrates a comparison of data using techniques describedherein.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the embodiments illustrated inthe drawings and specific language will be used to describe the same. Itwill nevertheless be understood that no limitation of the scope of theinvention is thereby intended. Any alterations and further modificationsin the described embodiments, and any further applications of theprinciples of the invention as described herein are contemplated aswould normally occur to one skilled in the art to which the inventionrelates.

With reference to FIG. 1, a schematic of a robot 50 which includes anumber of moveable robot components 52 useful for the robot to effect anaction. In the illustrated embodiment, the robot 50 is depicted asinteracting with a target 54 using an effector 56, but it will beunderstood that the robot may take on other roles that may or may notinvolve physical interaction with objects, such as those in which therobot 50 is limited to observations only. The robot 50 can be mountedupon a stationary base as illustrated in FIG. 1, but other forms arealso contemplated such as those involving a mobile robot. The robotcomponents 52 can take any variety of forms such as arms, links, beams,etc which can be used to position the effector 56. The robot 50 caninclude any number of moveable components 52 which can take on differentsizes, shapes, and other features. The components 52, furthermore, canbe interconnected with one another through any variety of usefulmechanisms such as links and/or gears 58, to set forth just twoexamples. The components 52 can be actuated via any suitable actuationdevice 60 such as electric actuators, pneumatic or hydraulic pistons,electromechanical actuators, etc. The effector 56 can take any varietyof forms such as a gripper, suction effector, belt, etc. As will beappreciated, a sensor 62 can be used to detect an operating condition ofthe robot 50. Such a sensor 62 can be part of the robot 50, can becoupled with the robot 50, or can be remote from the robot 50. To setforth just a few nonlimiting examples, the sensor 62 can be associatedwith an actuator 60 useful to enable movement of the robot, such as acurrent sensor, hydraulic pressure sensor, etc. In additional and/oralternative forms the sensor 62 can be an independent sensor used todetect a position, orientation, operational condition, etc of the robot.For example, a vibration sensor 62 could be used to sense condition ofthe robot. In still further alternative and/or additional forms, thesensor 62 could be a camera used to detect an electromagnetic conditionof the robot 50.

The robot 50 can be coupled to a computing device 55 which can be localto the robot 50, or stationed at a remote location. Such computingdevice 55 can be used to control, observe, and/or estimate conditions ofthe robot 50, among other potential uses. Embodiments of the computingdevice 55 can be used to detect the relative health of the robot 50 aswill be described further below. It will be appreciated that embodimentscan also be applied to other mechanically moveable devices whether ornot of the robotic kind.

Turning now to FIG. 2, and with continued reference to FIG. 1, aschematic diagram is depicted of a computing device 55 useful incontrolling, observing, estimating conditions and/or assessing thehealth of the robot 50. Computing device 55 includes a processing device64, an input/output device 66, memory 68, and operating logic 70.Furthermore, computing device 55 can be configured to communicate withone or more external devices 72.

The input/output device 66 may be any type of device that allows thecomputing device 55 to communicate with the external device 72. Forexample, the input/output device may be a network adapter, RF device,network card, or a port (e.g., a USB port, serial port, parallel port,VGA, DVI, HDMI, FireWire, CAT 5, or any other type of port). Theinput/output device 66 may be comprised of hardware, software, and/orfirmware. It is contemplated that the input/output device 66 includesmore than one of these adapters, cards, or ports.

The external device 72 may be any type of device that allows data to beinputted or outputted from the computing device 55. In one non-limitingexample the external device 72 is one or more of the sensors 62. To setforth just a few additional non-limiting examples, the external device72 may be another computing device, a server, a printer, a display, analarm, an illuminated indicator, a keyboard, a mouse, mouse button, or atouch screen display. Furthermore, it is contemplated that the externaldevice 72 may be integrated into the computing device 55. For example,the computing device 55 may be a smartphone, a laptop computer, or atablet computer. It is further contemplated that there may be more thanone external device in communication with the computing device 55. Theexternal device can be co-located with the computing device 55 oralternatively located remotely from the computer.

Processing device 64 can be of a programmable type, a dedicated,hardwired state machine, or a combination of these; and can furtherinclude multiple processors, Arithmetic-Logic Units (ALUs), CentralProcessing Units (CPUs), or the like. For forms of processing device 64with multiple processing units, distributed, pipelined, and/or parallelprocessing can be utilized as appropriate. Processing device 64 may bededicated to performance of just the operations described herein or maybe utilized in one or more additional applications. In the depictedform, processing device 64 is of a programmable variety that executesalgorithms and processes data in accordance with operating logic 70 asdefined by programming instructions (such as software or firmware)stored in memory 68. Alternatively or additionally, operating logic 70for processing device 64 is at least partially defined by hardwiredlogic or other hardware. Processing device 64 can be comprised of one ormore components of any type suitable to process the signals receivedfrom input/output device 66 or elsewhere, and provide desired outputsignals. Such components may include digital circuitry, analogcircuitry, or a combination of both.

Memory 68 may be of one or more types, such as a solid-state variety,electromagnetic variety, optical variety, or a combination of theseforms. Furthermore, memory 68 can be volatile, nonvolatile, or a mixtureof these types, and some or all of memory 68 can be of a portablevariety, such as a disk, tape, memory stick, cartridge, or the like. Inaddition, memory 68 can store data that is manipulated by the operatinglogic 70 of processing device 64, such as data representative of signalsreceived from and/or sent to input/output device 66 in addition to or inlieu of storing programming instructions defining operating logic 70,just to name one example. As shown in FIG. 2, memory 68 may be includedwith processing device 64 and/or coupled to the processing device 64.

The operating logic 70 can include the algorithms and steps of thecontroller, whether the controller includes the entire suite ofalgorithms necessary to effect movement and actions of the robot 50, orwhether the computing device 55 includes just those necessary to assessthe health of the robot 50. The operating logic can be saved in a memorydevice whether of the volatile or nonvolatile type, and can be expressedin any suitable type such as but not limited to source code, objectcode, and machine code.

Turning now to FIG. 3, one embodiment of a system useful in detecting ahealth condition of the robot 50 is illustrated. Monitoring the healthof a robot can be important in preventing unexpected robot failures andbreakdowns. Monitoring the condition of any of the robot components 52,actuation device 60, sensors 62, and/or interactions of any of theseitems, assists in determining the overall health of the robot 50. Injust one nonlimiting example, monitoring for backlash can be used toassess a health condition of a robot. In order to observe degradationsand predict failures, a model can be trained with signals obtained fromthe robot 50 (or an analogous robot used for that purpose) when therobot is in a healthy or baseline state. The training stage associatedwith building a model usually consists of data collection 74 (which caninclude some amount of preprocessing), feature extraction 76, comparisonof data 78, and a decision making step 80, which distinguishes betweendata corresponding to a healthy or abnormal state. One or more computingdevices 55 can be used in any of the data collection 74, featureextraction 76, alignment of data 78 in a common environment, comparisonof data and decision making 80. As will also be appreciated, sensors 62can be used to measure relevant details and provide data for the datacollection 74.

Once a model has been trained with a healthy/baseline robot dataset(also sometimes referred to as a training dataset), signals obtainedfrom the robot when in later operations can be compared with the trainedmodel, but caution must be exercised. As described herein, a robot canbe subjected to a first set of operations characterized by a certainsequence of movement of robot components 52 (and/or robot effectors 56).The robot can also be subjected to a second set of operationscharacterized by different sequence of movements of the same ordifferent combination of robot components 52 (and/or robot effectors56). The operations can therefore be the same as the training data, butneed not always be the same. If features determined from operation ofthe robot diverge significantly from features present in the model, therobot might be considered to be in an abnormal state and a futurefailure can be predicted, but care must be taken to ignore differencesdue entirely to operation changes and not a change in health.Embodiments described below can be used to assist comparing operationaldata with health/baseline data of the robot regardless if theoperational movements of the robot are the same as the movements used toassess the health/baseline robot. Embodiments described herein introducea manifold alignment step after the feature extraction 76 and beforemaking the final decision 78.

One technique of extracting relevant features from data collected viathe sensor 62 involves the use of principal component analysis. Anothertechnique of extracting features in step 76 prior to manifold alignmentemploys the use of short-time Fourier transform (STFT) of the robotsignals and combinations are used for feature extraction. Referencebelow to principal component analysis is just an example and is notintended to be limiting, as the STFT approach can also be used toextract features. Thus, mention of the term “feature” herein can referto features extracted using STFT as well as principal componentanalysis. As will be appreciated, principal component analysis can beperformed on a dataset, whether the data represents raw or calculateddata, and/or represents data collected from the sensor that is furtherprocessed/calculated and/or used in an estimation routine either byitself or in conjunction with other data. Let v₁, v₂, . . . , v_(p)denote the principal components of the features X∈

^(NXP) extracted from the training signals. These principal components(vectors) span the subspace, which represents the training data. Now,let Y∈

^(NXP) be the features extracted from the test data with the principalcomponents u₁, u₂, . . . , u_(p). If X and Y are the features from twodifferent datasets with the same robot operation, v₁, v₂, . . . , v_(p)is able to represent X and Y, and Y can be compared with X in thissubspace. As a result, divergence of Y from X is the indicator of ananomaly.

If X and Y are features corresponding to two different robot operations,v₁, v₂, . . . , v_(p) may not be capable of representing Y. Thus, Y canbe detected as an anomaly even though it may be normal behavior. Inembodiments of the application herein the subspaces of X and Y areconsidered two points on a manifold. The assumption is that when theoperation changes, the subspace representing the dataset moves on amanifold. In order to compensate for this movement, the features of thetwo datasets can be aligned before comparison. Manifold alignment isused in the alignment when these datasets are assumed to share a commonunderlying structure, by embedding each input set into a shared latentmanifold space. Techniques described herein find the common subspacebetween test and training dataset through unsupervised techniques suchas manifold alignment, the alignment of which in some embodiments caninclude solving an optimization problem.

FIG. 3 depicts an embodiment in which an optimization problem is solvedto assist in determining a common subspace of the test and trainingdata. Non-limiting techniques of determining a common subspace includeDomain Adaptation which can include, to set forth just a few examples,manifold alignment, locally linear embedding (LLE), low-rank embedding(LRE), and low-rank alignment (LRA). It is also possible to make atransformation of the features of one of the datasets into the subspaceof the other of the datasets. LRA is a variation of LRE and can be usedto find a common subspace between v₁, v₂, . . . , v_(p) and u₁, u₂, . .. , u_(p) (such as the features extracted using STFT). One such approachto determining LRA is described in the paper “Aligning Mixed Manifolds”by Boucher, Carey, Mahadevan, and Dyar, the contents of which areincorporated herein by reference.

The approach to determining LRA is as follows. To begin the alignment, Xis decomposed using singular value decomposition (SVD), X=USV^(T). Thecolumns of V and S are partitioned into V=[V₁V₂] and S=[S₁S₂] accordingto the sets I₁={i:s_(i)>1∀s_(i)∈S} and I₂={i:s_(i)≤1∀s_(i)∈S}.

An optimal closed-form solution to the loss function used in LRE,

${\min\limits_{R}{\frac{1}{2}{{{X - {XR}}}}_{F}^{2}}} + {\lambda{R}_{*}}$is R^((X))=V₁(I−S₁ ⁻²)V₁ ^(T).

R(X), R(Y) are then calculated independently. In one form thecalculations can be performed in parallel to reduce computation time.Block matrices R,C∈

^(NXN) are defined as

$R = {{\begin{bmatrix}R^{(X)} & 0 \\0 & R^{(Y)}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu} C} = {\begin{bmatrix}0 & C^{({X,Y})} \\C^{({Y,X})} & 0\end{bmatrix}\mspace{14mu}{and}}}$${F \in {{\mathbb{R}}^{N \times d}\mspace{14mu}{as}\mspace{14mu} F}} = {\begin{bmatrix}F^{(X)} \\F^{(Y)}\end{bmatrix}.}$

The second step of LRA is to calculate the embedding F of X,Y byminimizing the following loss function:

${{{\mathfrak{Z}}(F)} = {{\left( {1 - \mu} \right){{F - {RF}}}_{F}^{2}} + {\mu{\sum\limits_{i,{j = 1}}^{N}\;{{{F_{i} - F_{j}}}^{2}C_{i,j}}}}}},$where μ∈[0,1] is the hyperparameter that controls the importance ofinter-set correspondences. The first term in the equation immediatelyabove accounts for local geometry within each of the datasets, and thesecond term in the equation immediately above accounts for thecorrespondence between sets. The loss function can be reduced to a sumof matrix traces as follows:

$\begin{matrix}{{{\mathfrak{Z}}(F)} = {{\left( {1 - \mu} \right){{tr}\left( {\left( {F - {RF}} \right)^{T}\left( {F - {RF}} \right)} \right)}} + {\mu{\sum\limits_{k = 1}^{d}\;{\sum\limits_{i,{j = 1}}^{N}\;{{{F_{i,k} - F_{j,k}}}_{2}^{2}W_{i,j}}}}}}} \\{= {{\left( {1 - \mu} \right){{tr}\left( {\left( {\left( {I - R} \right)F} \right)^{T}\left( {I - R} \right)F} \right)}} + {2\mu{\sum\limits_{k = 1}^{d}{F_{i,k}^{T}{LF}_{j,k}}}}}} \\{= {{\left( {1 - \mu} \right){{tr}\left( {{F^{T}\left( {I - R} \right)}^{T}\left( {I - R} \right)F} \right)}} + {2\;\mu\;{{{tr}\left( {F^{T}{LF}} \right)}.}}}}\end{matrix}$In a similar manner as LLE and LRE, a constraint F^(T)F=I can beintroduced to ensure that the minimization of the loss function is awell-posed problem. Thus:

${{\underset{{F:{F^{T}F}} = I}{\arg\;\min}\mspace{14mu}{\mathfrak{Z}}} = {{\underset{{F:{F^{T}F}} = I}{\arg\;\min}\left( {1 - \mu} \right){{tr}\left( {F^{T}{MF}} \right)}} + {2\;\mu\;{{tr}\left( {F^{T}{LF}} \right)}}}},$Where M=(I−R)^(T)(I−R). To construct a loss function from the equationimmediately above, take the right side and introduce the Lagrangemultiplier Λ,

ℒ(F, Λ) = (1 − μ)tr(F^(T)MF) + 2μ tr(F^(T)LF) + ⟨Λ, F^(T)F − I⟩.To minimize the equation immediately above, find the roots of itspartial derivatives:

$\frac{\partial\mathcal{L}}{\partial F} = {{{2\left( {1 - \mu} \right){MF}} + {4\mu\; L\; F} - {2\;\Lambda\; F}} = 0}$$\frac{\partial\mathcal{L}}{\partial\Lambda} = {{{F^{T}F} - I} = 0.}$From the above system of equations, we are left with the matrixeigenvalue problem as follows: ((1−μ)M+2 μL)F=ΛF and F^(T)F=1. To solvethe minimization problem above, calculate the d smallest non-zeroeigenvectors of the following matrix: (1'μ)M+2 μL.

The eigenproblem can be solved efficiently because the matrix M+L isguaranteed to be symmetric, positive semindefinite (PSD), and sparse.These particular properties arise from

${{M + L} = {\begin{bmatrix}\left( {I - R^{(X)}} \right)^{2} & 0 \\0 & \left( {I - R^{(Y)}} \right)^{2}\end{bmatrix} + \begin{bmatrix}D^{X} & {- C^{({X,Y})}} \\\left( {- C^{({X,Y})}} \right)^{T} & D^{Y}\end{bmatrix}}},$where by construction

$D = \begin{bmatrix}D^{X} & 0 \\0 & D^{Y}\end{bmatrix}$is a PSD diagonal matrix and C^((X,Y)) is a sparse matrix. The followingis an algorithm to compute Low Rank Alignment:

Algorithm 1: Low Rank Alignment Input: data matrices X, Y , embeddingdimension d, correspondence matrix C^((X,Y)) and weight μ. Output:embeddings matrix F. Step 0: Column normalize X & Y (optional butrecommended if X and Y differ largely in scale). Step 1: Compute thereconstruction coefficient matrices R^((X)) , R^((Y)):  U SV^(T) =SVD(X)  R^((X)) = V₁(I − S₁ ⁻²)V₁ ^(T)  Û Ŝ{circumflex over (V)}^(T) =SVD(Y)  R^((Y)) = {circumflex over (V)}₁(I − Ŝ₁ ⁻²){circumflex over(V)}₁ ^(T) Step 2: Set F equal to d smallest eigenvectors of the matrixin (1 − μ)M + 2μL

As will be appreciated, once the datasets have been projected onto acommon subspace, a comparison can be made between the healthy/baselinetraining data and the operational data. To set forth just a fewnon-limiting embodiments to assist in the comparison, classifiers can beused. Classifiers such as but not limited to support vector machines andvarious distance techniques such as L2-norm can be used.

FIGS. 5 and 6 illustrate a few results using the techniques describedherein. FIG. 5 represents a comparison of data with and without usingmanifold alignment when there are operation changes only to the robot50. The top plot of FIG. 5 shows the resulting difference if theoperation of the robot is not the same as that used to perform thetraining, and furthermore represent the resulting difference if theprincipal component analysis results are not projected onto a commonsubspace. In the top plot of FIG. 5 are circled the operation changeswhich are detected as an abnormality when manifold alignment is not usedand in which principal component analysis is used on STFT output. Notethe large L2 norm of the difference between X and Y. The bottom plot inFIG. 5 shows results using STFT and in which the L2-norm of thedifference between the two datasets X and Y is shown after projectingonto the common subspace using manifold alignment. Notice the relativelysmall scale of the difference between X and Y in the bottom plot of FIG.5 and the lack of sensitivity to the operation change.

FIG. 6 depicts another experiment, this time one in which no operationchanges are used but a robot failure is introduced. The top plot of FIG.6 is again used to illustrate the L2-norm where datasets are notprojected onto a common subspace and in which principal componentanalysis is used on STFT output. The bottom plot of FIG. 6 illustratesresults using STFT and in which the L2-norm of the datasets areprojected onto a common subspace using manifold alignment. FIG. 6illustrates that both techniques can be used to determine the robotfailure. Combining the results of both FIGS. 5 and 6, it can be seenthat the techniques herein useful to project datasets onto a commonsubspace are relatively insensitive to changes in robot operations, andare still sensitive to detect robot failures.

Returning now to FIGS. 2 and 3, and with continuing reference to FIG. 1,various other aspects of how data is collected, calculated, shared, andacted on will also be described. The robot 50 and a computing device 55that implements the subspace projection/alignment techniques describedherein (referred to as a diagnostic device) can both be locally locatedrelative to each other, but such configuration is not a requirement. Inone form data related to the operation of the robot (e.g. via sensor 62)can be transmitted directly to the diagnostic device, whether locatedlocally or remote from one another. For example, the data can betransmitted via wired and/or wireless techniques which can employ anytype of data format (e.g. packet transmission, pulse width modulation,etc). In another alternative and/or additional form data from the sensor62 can be delivered to a data cloud to be accessed by the diagnosticdevice. The data transmitted can represent raw data collected by thesensor, but in some embodiments the data can represent measurements thathave been preprocessed prior to deliver to the diagnostic device (e.g.conversion from instrumentation counts into a unit of measure, etc)and/or further calculated, processed, etc. The data can be processed bya single computing device 55 to determine health of the robot 50, but inother forms the computing device 55 can be a distributed computingdevice in which different computations/analysis/steps are performed byvarious devices spread across the distributed resource. As will beunderstood, the diagnostic device can be resident with the robot in someforms, and can be remote in others.

Data calculated from the classifier (e.g. the regression calculation(e.g. the L2-norm)) after the datasets have been projected to a commonsubspace can be shared back to the local area of the robot, it can beused in a database for inspections, and/or it can be stored for laterevaluation, among other potential uses. Techniques herein can utilize acriteria useful to identify a robot failure. To set forth just onenon-limiting example, an absolute value of the distance calculated bythe L2-norm can be used to define the criteria that defines an unhealthyrobot. In other forms a relative value calculated by the L2-norm canalso be used as the criteria, such as an excursion from a value such asa mean, to set forth just a few non-limiting examples. Other similar ordifferent determinations can be made for other classifiers.

An indication can be set if the data calculated from the classifierexceeds a criteria (e.g. an indication can be set if absolutevalue/relative value/etc of the distance (e.g. L2-norm) exceeds acriteria). Such an indication can include a textual warning entered intoa log, a visible warning on a computer screen, an audible warning in thelocal area of the robot, a warning light, etc. Such indication can be ageneric warning to generally inspect the robot, while other indicationscan be specific as to replacing or repairing specific robot components.In short, any number of techniques, both passive and active, can be usedto automatically flag an unhealthy condition of the robot.

One aspect of the present application includes an apparatus comprising:a robot having a component structured to be subject to relativemechanical movement during operation of the robot, a sensor coupled withthe robot configured to detect an operating condition of the component,a diagnostic device configured to receive a measurement from the sensorand including a memory having a training data, the diagnostic deviceconfigured to distinguish between a change in health of the robot andchange in operation of the robot and having computer based instructionsstructured to: compute a principal component analysis of the measurementfrom the sensor to provide an operation data, determine a commonsubspace between the training data and the operation data, and utilize aclassifier trained on the training data and applied on the operationdata in the common subspace.

A feature of the present application provides wherein the diagnosticdevice includes a computer processing device and a computer memory,wherein the memory includes the computer based instructions that whenexecuted by the computer processing device is operative to provide anoutput signal indicative of the distance between the training data andthe operation data in the common subspace, the output signal havinggreater sensitivity to changes in health of the robot and lessersensitivity to changes in operation of the robot.

Another feature of the present application provides wherein diagnosticdevice is further structured to determine the common subspace viaunsupervised domain adaptation.

Still another feature of the present application provides whereindiagnostic device is further structured to determine the common subspacevia unsupervised domain adaptation.

Yet another feature of the present application provides wherein theunsupervised domain adaptation is performed using manifold alignment.

Still yet another feature of the present application provides whereinthe manifold alignment is performed using low rank alignment.

Yet still another feature of the present application provides wherein ameasurement of the sensor is correlated with the relative mechanicalmovement of the robot during an operational movement of the robot, andwherein the diagnostic device is in information communication with atransceiver configured to receive the measurement from the sensor

A further feature of the present application provides wherein thedistance is computed using an L2-norm measure, and wherein a degradedhealth condition of the robot can be determined by a comparison of theL2-norm computed distances to a criteria.

Another aspect of the present application provides an apparatuscomprising: a robotic diagnostic device which includes at least oneinput and one output and configured to assist in the determination of ahealth of a robot having moving mechanical components by distinguishingchanges in operation of the robot from a change in health of the robot,the robotic diagnostic device structured to align data features of atraining data and data features of an operation data in a commonsubspace, and utilize a classifier within the common subspace trained onthe training data and applied to the operation data, wherein theoperation data includes features related to a mechanical movement of therobot which is targeted for health evaluation and the training dataincludes features related to a reference mechanical movement that theoperation data is to be compared against.

A feature of the present application provides wherein the roboticdiagnostic device includes a computer processing device and a computermemory, wherein the memory includes instructions that when executed bythe computer processing device is operative to provide an output signalindicative of the change in health of the robot, and wherein the roboticdiagnostic device is structured to compute the distance such that thedistance is responsive to changes in health of the robot and insensitiveto changes in operation of the robot.

Another feature of the present application provides wherein theclassifier is a support vector machine.

Still another feature of the present application provides wherein therobotic diagnostic device is structured to align features of a trainingdata and features of an operation data in a common subspace using domainadaptation.

Yet another feature of the present application provides wherein therobotic diagnostic device is structured to use unsupervised transferlearning.

Still yet another feature of the present application provides whereinthe distance within the common subspace between the training data andthe operation data is computed using an L2-norm.

Yet still another feature of the present application further includes arobot coupled with the robotic diagnostic device, the robot having amoveable component and a sensor structured to take data which iscorrelated with a movement of the moveable component, and wherein therobotic diagnostic device is configured to set an indication when thedistance exceeds a health criteria.

Yet another aspect of the present application provides a methodcomprising: effecting relative movement of a first robotic moveablemember relative to a second robotic moveable member, collecting datarelated to the relative movement, extracting a movement feature of thedata so that the movement feature can be compared to a training feature,projecting at least one of the movement feature and the training featureonto a common subspace which includes both the movement feature and thetraining feature to form an evaluation movement feature and anevaluation training feature, and utilizing a classifier trained on theprojected training feature and applied to the projected movementfeature.

A feature of the present application further includes comparing thedistance to a health criteria and setting an indication if the distanceexceeds the health criteria.

Another feature of the present application provides wherein thedetermining includes computing an L2-norm between the evaluationmovement feature and the evaluation training feature.

Still another feature of the present application provides utilizing aclassifier includes using a support vector machine.

Yet another feature of the present application provides wherein theprojecting includes an unsupervised alignment of the movement featureand the training feature.

Yet still another feature of the present application further includessetting an indication when the distance exceeds a threshold.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, the same is to be considered asillustrative and not restrictive in character, it being understood thatonly the preferred embodiments have been shown and described and thatall changes and modifications that come within the spirit of theinventions are desired to be protected. It should be understood thatwhile the use of words such as preferable, preferably, preferred or morepreferred utilized in the description above indicate that the feature sodescribed may be more desirable, it nonetheless may not be necessary andembodiments lacking the same may be contemplated as within the scope ofthe invention, the scope being defined by the claims that follow. Inreading the claims, it is intended that when words such as “a,” “an,”“at least one,” or “at least one portion” are used there is no intentionto limit the claim to only one item unless specifically stated to thecontrary in the claim. When the language “at least a portion” and/or “aportion” is used the item can include a portion and/or the entire itemunless specifically stated to the contrary. Unless specified or limitedotherwise, the terms “mounted,” “connected,” “supported,” and “coupled”and variations thereof are used broadly and encompass both direct andindirect mountings, connections, supports, and couplings. Further,“connected” and “coupled” are not restricted to physical or mechanicalconnections or couplings.

What is claimed is:
 1. An apparatus comprising: a robot having acomponent structured to be subject to relative mechanical movementduring operation of the robot; a sensor coupled with the robotconfigured to detect an operating condition of the component; adiagnostic device configured to receive a measurement from the sensorand including a memory having a training data, the diagnostic deviceconfigured to distinguish between a change in health of the robot andchange in operation of the robot and having computer based instructionsstructured to: compute a principal component analysis of the measurementfrom the sensor to provide an operation data; determine a commonsubspace between the training data and the operation data; and utilize aclassifier trained on the training data and applied on the operationdata in the common subspace.
 2. The apparatus of claim 1, wherein thediagnostic device includes a computer processing device and a computermemory, wherein the memory includes the computer based instructions thatwhen executed by the computer processing device is operative to providean output signal indicative of the distance between the training dataand the operation data in the common subspace, the output signal havinggreater sensitivity to changes in health of the robot and lessersensitivity to changes in operation of the robot.
 3. The apparatus ofclaim 1, wherein diagnostic device is further structured to determinethe common subspace via unsupervised domain adaptation.
 4. The apparatusof claim 3, wherein the unsupervised domain adaptation is performedusing manifold alignment.
 5. The apparatus of claim 3, wherein themanifold alignment is performed using low rank alignment.
 6. Theapparatus of claim 5, wherein a measurement of the sensor is correlatedwith the relative mechanical movement of the robot during an operationalmovement of the robot, and wherein the diagnostic device is ininformation communication with a transceiver configured to receive themeasurement from the sensor.
 7. The apparatus of claim 5, wherein thedistance is computed using an L2-norm measure, and wherein a degradedhealth condition of the robot can be determined by a comparison of theL2-norm computed distances to a criteria.
 8. An apparatus comprising: arobotic diagnostic device which includes at least one input and oneoutput and configured to assist in the determination of a health of arobot having moving mechanical components by distinguishing changes inoperation of the robot from a change in health of the robot, the roboticdiagnostic device structured to align data features of a training dataand data features of an operation data in a common subspace, and utilizea classifier within the common subspace trained on the training data andapplied to the operation data, wherein the operation data includesfeatures related to a mechanical movement of the robot which is targetedfor health evaluation and the training data includes features related toa reference mechanical movement that the operation data is to becompared against.
 9. The apparatus of claim 8, wherein the roboticdiagnostic device includes a computer processing device and a computermemory, wherein the memory includes instructions that when executed bythe computer processing device is operative to provide an output signalindicative of the change in health of the robot, and wherein the roboticdiagnostic device is structured to compute the distance such that thedistance is responsive to changes in health of the robot and insensitiveto changes in operation of the robot.
 10. The apparatus of claim 8,wherein the classifier is a support vector machine.
 11. The apparatus ofclaim 10, wherein the robotic diagnostic device is structured to alignfeatures of a training data and features of an operation data in acommon subspace using domain adaptation.
 12. The apparatus of claim 8,wherein the robotic diagnostic device is structured to use unsupervisedtransfer learning.
 13. The apparatus of claim 12, wherein the distancewithin the common subspace between the training data and the operationdata is computed using an L2-norm.
 14. The apparatus of claim 8, whichfurther includes a robot coupled with the robotic diagnostic device, therobot having a moveable component and a sensor structured to take datawhich is correlated with a movement of the moveable component, andwherein the robotic diagnostic device is configured to set an indicationwhen the distance exceeds a health criteria.
 15. A method comprising:effecting relative movement of a first robotic moveable member relativeto a second robotic moveable member; collecting data related to therelative movement; extracting a movement feature of the data so that themovement feature can be compared to a training feature; projecting atleast one of the movement feature and the training feature onto a commonsubspace which includes both the movement feature and the trainingfeature to form an evaluation movement feature and an evaluationtraining feature; and utilizing a classifier trained on the projectedtraining feature and applied to the projected movement feature.
 16. Themethod of claim 15, which further includes comparing the distance to ahealth criteria and setting an indication if the distance exceeds thehealth criteria.
 17. The method of claim 16, wherein the determiningincludes computing an L2-norm between the evaluation movement featureand the evaluation training feature.
 18. The method of claim 17, whereinutilizing a classifier includes using a support vector machine.
 19. Themethod of claim 17, wherein the projecting includes an unsupervisedalignment of the movement feature and the training feature.
 20. Themethod of claim 19, which further includes setting an indication whenthe distance exceeds a threshold.